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Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods [article]

Guo-Jun Qi, Jiebo Luo
2021 arXiv   pre-print
In this paper, we will review the recent progresses on these two major categories of methods.  ...  Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is expensive  ...  INTRODUCTION T HIS paper aims at a comprehensive survey of recent progresses on unsupervised and semi-supervised methods addressing the challenges of training models with a small number of labeled data  ... 
arXiv:1903.11260v2 fatcat:hjya3ojzmfh7nnldhqkdx6o37a

A survey on data‐efficient algorithms in big data era

Amina Adadi
2021 Journal of Big Data  
Finally, the survey delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning.  ...  Then, it presents a comprehensive review of existing data-efficient methods and systematizes them into four categories.  ...  A recent survey by Dimensional Research shows that 96 % of enterprises encounter data quality and labeling challenges in ML projects [54] .  ... 
doi:10.1186/s40537-021-00419-9 fatcat:v4uahsvhlzdldlxqf24bshmja4

Small Sample Learning in Big Data Era [article]

Jun Shu, Zongben Xu, Deyu Meng
2018 arXiv   pre-print
This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures.  ...  As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years.  ...  ., Pseudo-label method This strategy is specifically imposed on small labeled sample set while sufficient unlabeled sample cases (i.e., semi-supervised data).  ... 
arXiv:1808.04572v3 fatcat:lqqzzrmgfnfb3izctvdzgopuny

Event Prediction in the Big Data Era: A Systematic Survey [article]

Liang Zhao
2020 arXiv   pre-print
This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era.  ...  Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth.  ...  CONCLUSION This survey has presented a comprehensive survey of existing methodologies developed for event prediction methods in the big data era.  ... 
arXiv:2007.09815v3 fatcat:ypmjm3n3xjbcjbzdlhowdkaona

An Overview of Perception and Decision-Making in Autonomous Systems in the Era of Learning [article]

Yang Tang, Chaoqiang Zhao, Jianrui Wang, Chongzhen Zhang, Qiyu Sun, Weixing Zheng, Wenli Du, Feng Qian, Juergen Kurths
2020 arXiv   pre-print
Finally, we examine the several challenges and promising directions discussed and concluded in related research for future works in the era of computer science, automatic control, and robotics.  ...  In this review, we focus on the applications of learning-based approaches in perception and decision-making in autonomous systems, which is different from previous reviews that discussed traditional methods  ...  Therefore, instead of using the error between depth estimation and ground-truth as the supervisory signals, attempts have been made to propose unsupervised or semi-supervised methods based on the new constraints  ... 
arXiv:2001.02319v3 fatcat:z3zhp2cyonfqtlttl2y57572uy

Education policy research in the big data era: Methodological frontiers, misconceptions, and challenges

Yinying Wang
2017 Education Policy Analysis Archives  
Drawing on the recent progress of using big data in public policy and computational social science research, this commentary discusses how to approach big data and how big data can be used in education  ...  This commentary concludes with a discussion on developing interdisciplinary research capacity and addressing the privacy concerns and ethical conundrums as we explore a research agenda of using big data  ...  ., unsupervised) or semi-automated (i.e., supervised) (Roberts et al., 2016) .  ... 
doi:10.14507/epaa.25.3037 fatcat:hlwiigzjerb2foq4unwd7vmodi

Machine Learning Modeling: A New Way to do Quantitative Research in Social Sciences in the Era of AI

Jiaxing Zhang, Shuaishuai Feng
2021 Journal of Web Engineering  
Improvements in big data and machine learning algorithms have helped AI technologies reach a new breakthrough and have provided a new opportunity for quantitative research in the social sciences.  ...  Researchers should adopt an objective attitude and make sure that they know how to combine traditional methods with new methods in their research based on their needs.  ...  With the rise of the era of big data and computational social sciences, big data sets and new computational tools are bringing new life to the social sciences.  ... 
doi:10.13052/jwe1540-9589.2023 fatcat:xecavw5zvvhvng3hlm4nps2wom

Knowledge Organization, Data and Algorithms: The New Era of Visual Representations

Viviane Clavier
2019 Knowledge organization  
The paper adopts a theoretical and historical perspective and focuses on the consequences of the changes in the volume of data generated by data production on the KO models.  ...  But now, these tools are accessible to a greater number of users.  ...  As a follow-up to these methods, text mining includes statistical data processing and tasks such as supervised or unsupervised classification (Ibekwe-SanJuan 2007).  ... 
doi:10.5771/0943-7444-2019-8-615 fatcat:ofp4ndoww5csrditqfnlo5iyra

Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner

Emmanuel Dupoux
2018 Cognition  
Recently, spectacular progress in the engineering science, notably, machine learning and wearable technology, offer the promise of revolutionizing the study of cognitive development.  ...  This implies that (1) accessible but privacy-preserving repositories of home data be setup and widely shared, and (2) models be evaluated at different linguistic levels through a benchmark of psycholinguist  ...  Within this scenario, we claim that recent advances in AI and big data now make the reverse engineering roadmap actionnable.  ... 
doi:10.1016/j.cognition.2017.11.008 pmid:29324240 fatcat:2tjqez2gerdoxh5rn2nxb27pti

Integrative methods for analyzing big data in precision medicine

Vladimir Gligorijević, Noël Malod-Dognin, Nataša Pržulj
2016 Proteomics  
We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies.  ...  We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics.  ...  Innovation (CDI) OIA-1028394, the ARRS project J1-5454, and the Serbian Ministry of Education and Science Project III44006.  ... 
doi:10.1002/pmic.201500396 pmid:26677817 fatcat:rwqiuxxgmffrppkz2ccj7ffm5m

A survey of machine learning for big data processing

Junfei Qiu, Qihui Wu, Guoru Ding, Yuhua Xu, Shuo Feng
2016 EURASIP Journal on Advances in Signal Processing  
In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing.  ...  Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data.  ...  To clarify what the big data refers to, several good surveys have been presented recently and each of them views the big data from different perspectives, including challenges and opportunities [4] ,  ... 
doi:10.1186/s13634-016-0355-x fatcat:yrrg335535fmnibwu3k4iyzyxm

Satellite Communications in the New Space Era: A Survey and Future Challenges

Oltjon Kodheli, Eva Lagunas, Nicola Maturo, Shree Krishna Sharma, Bhavani Shankar, Jesus Fabian Mendoza Montoya, Juan Carlos Merlano Duncan, Danilo Spano, Symeon Chatzinotas, Steven Kisseleff, Jorge Querol, Lei Lei (+2 others)
2020 IEEE Communications Surveys and Tutorials  
Finally, a number of future challenges and the respective open research topics are described.  ...  Satellite communications (SatComs) have recently entered a period of renewed interest motivated by technological advances and nurtured through private investment and ventures.  ...  Out of these, supervised learning requires the labelled training data-set while the unsupervised learning does not require the labelled data-sets.  ... 
doi:10.1109/comst.2020.3028247 fatcat:qdsitas5xjhwtfhs6nin3eus5q

Computational discovery of energy materials in the era of big data and machine learning: a critical review

Ziheng Lu
2021 Materials Reports: Energy  
In this report, recent advances in material discovery methods are reviewed for energy devices.  ...  Together these recent innovations in computational chemistry, data informatics, and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics  ...  Chi Chen and Dr. Zhaofu Zhang for the helpful discussions.  ... 
doi:10.1016/j.matre.2021.100047 fatcat:qookvmaqfze7zogocfqrbu7awi

The Flare Likelihood and Region Eruption Forecasting (FLARECAST) Project: Flare forecasting in the big data machine learning era [article]

M. K. Georgoulis, D. S. Bloomfield, M. Piana, A. M. Massone, M. Soldati, P. T. Gallagher, E. Pariat, N. Vilmer, E. Buchlin, F. Baudin, A. Csillaghy, H. Sathiapal (+16 others)
2021 arXiv   pre-print
(14) different ML techniques, also on equal footing, to optimize the immense Big Data parameter space created by these many predictors; third, the establishment of a robust, three-pronged communication  ...  In spite of being one of the most intensive and systematic flare forecasting efforts to-date, FLARECAST has not managed to convincingly lift the barrier of stochasticity in solar flare occurrence and forecasting  ...  We are also indebted to the FLARECAST Steering Committee (Neal Hurlburt [Chair], Graham Barnes, Douglas Biesecker, Pedro Russo, and Silvia Villa) who devoted time and effort on the project and contributed  ... 
arXiv:2105.05993v1 fatcat:zqpug2junneqbei3iw4gvqkzhm

Artificial intelligence: a key to relieve China's insufficient and unequally-distributed medical resources

Xiangyi Kong, Bolun Ai, Yiming Kong, Lijuan Su, Yunzhou Ning, Newton Howard, Shun Gong, Chen Li, Jie Wang, Wan-Ting Lee, Jing Wang, Yanguo Kong (+2 others)
2019 American journal of translational research  
Also, we analyzed China's advantages in developing medical AI due to its huge medical big data and China government's powerful promotion policy.  ...  In this manuscript, we firstly reviewed the challenges faced by China in its health care reform.  ...  Table 2 , the types of machine learning algorithms in AI solutions can be divided into supervised learning, unsupervised leaning, and semi-supervised learning (reinforcement learning and transfer learning  ... 
pmid:31217843 pmcid:PMC6556644 fatcat:tbcqlmnu65e73chzgveegnfmlm
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